Overview

Dataset statistics

Number of variables14
Number of observations2460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory269.2 KiB
Average record size in memory112.1 B

Variable types

Numeric10
Categorical4

Alerts

away_team_hits is highly overall correlated with away_team_runsHigh correlation
away_team_runs is highly overall correlated with away_team_hitsHigh correlation
home_team_hits is highly overall correlated with home_team_runsHigh correlation
home_team_runs is highly overall correlated with home_team_hitsHigh correlation
start_time is highly overall correlated with game_typeHigh correlation
game_type is highly overall correlated with start_time and 2 other fieldsHigh correlation
home_team is highly overall correlated with game_type and 1 other fieldsHigh correlation
venue is highly overall correlated with game_type and 1 other fieldsHigh correlation
away_team is uniformly distributedUniform
home_team is uniformly distributedUniform
away_team_errors has 1407 (57.2%) zerosZeros
away_team_runs has 156 (6.3%) zerosZeros
date has 277 (11.3%) zerosZeros
home_team_errors has 1416 (57.6%) zerosZeros
home_team_runs has 130 (5.3%) zerosZeros

Reproduction

Analysis started2023-02-03 19:34:09.878385
Analysis finished2023-02-03 19:34:28.458657
Duration18.58 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

attendance
Real number (ℝ)

Distinct2374
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30370.704
Minimum8766
Maximum54449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:28.558713image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum8766
5-th percentile13971.15
Q122432
median30604.5
Q338396.25
95-th percentile45624.1
Maximum54449
Range45683
Interquartile range (IQR)15964.25

Descriptive statistics

Standard deviation9875.4667
Coefficient of variation (CV)0.32516424
Kurtosis-0.90285907
Mean30370.704
Median Absolute Deviation (MAD)8006
Skewness-0.052483633
Sum74711931
Variance97524843
MonotonicityNot monotonic
2023-02-03T13:34:28.745761image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27631 3
 
0.1%
41210 2
 
0.1%
41850 2
 
0.1%
13481 2
 
0.1%
44317 2
 
0.1%
34294 2
 
0.1%
39691 2
 
0.1%
36544 2
 
0.1%
22230 2
 
0.1%
26087 2
 
0.1%
Other values (2364) 2439
99.1%
ValueCountFrequency (%)
8766 1
< 0.1%
9393 1
< 0.1%
9890 1
< 0.1%
10068 1
< 0.1%
10072 1
< 0.1%
10114 1
< 0.1%
10115 1
< 0.1%
10117 1
< 0.1%
10251 1
< 0.1%
10283 1
< 0.1%
ValueCountFrequency (%)
54449 2
0.1%
54269 1
< 0.1%
53901 1
< 0.1%
53621 1
< 0.1%
53449 1
< 0.1%
53409 1
< 0.1%
53299 1
< 0.1%
53297 1
< 0.1%
53279 1
< 0.1%
52728 1
< 0.1%

away_team
Categorical

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Chicago Cubs
 
90
Los Angeles Dodgers
 
87
Cleveland Indians
 
86
Toronto Blue Jays
 
85
San Francisco Giants
 
84
Other values (25)
2028 

Length

Max length29
Median length19
Mean length16.694309
Min length12

Characters and Unicode

Total characters41068
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York Mets
2nd rowPhiladelphia Phillies
3rd rowMinnesota Twins
4th rowWashington Nationals
5th rowColorado Rockies

Common Values

ValueCountFrequency (%)
Chicago Cubs 90
 
3.7%
Los Angeles Dodgers 87
 
3.5%
Cleveland Indians 86
 
3.5%
Toronto Blue Jays 85
 
3.5%
San Francisco Giants 84
 
3.4%
Boston Red Sox 83
 
3.4%
Washington Nationals 83
 
3.4%
Baltimore Orioles 82
 
3.3%
Texas Rangers 82
 
3.3%
Cincinnati Reds 81
 
3.3%
Other values (20) 1617
65.7%

Length

2023-02-03T13:34:28.920355image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago 171
 
2.8%
angeles 168
 
2.8%
los 168
 
2.8%
san 165
 
2.7%
sox 164
 
2.7%
new 161
 
2.7%
york 161
 
2.7%
cubs 90
 
1.5%
dodgers 87
 
1.4%
cleveland 86
 
1.4%
Other values (57) 4646
76.6%

Most occurring characters

ValueCountFrequency (%)
a 3762
 
9.2%
3607
 
8.8%
s 3530
 
8.6%
e 3275
 
8.0%
i 3021
 
7.4%
o 2800
 
6.8%
n 2714
 
6.6%
t 2035
 
5.0%
r 1876
 
4.6%
l 1804
 
4.4%
Other values (36) 12644
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31395
76.4%
Uppercase Letter 5986
 
14.6%
Space Separator 3607
 
8.8%
Other Punctuation 80
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3762
12.0%
s 3530
11.2%
e 3275
10.4%
i 3021
9.6%
o 2800
8.9%
n 2714
8.6%
t 2035
 
6.5%
r 1876
 
6.0%
l 1804
 
5.7%
g 915
 
2.9%
Other values (14) 5663
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 670
11.2%
A 653
10.9%
B 492
 
8.2%
S 490
 
8.2%
R 489
 
8.2%
M 485
 
8.1%
T 410
 
6.8%
P 405
 
6.8%
D 330
 
5.5%
L 248
 
4.1%
Other values (10) 1314
22.0%
Space Separator
ValueCountFrequency (%)
3607
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37381
91.0%
Common 3687
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3762
 
10.1%
s 3530
 
9.4%
e 3275
 
8.8%
i 3021
 
8.1%
o 2800
 
7.5%
n 2714
 
7.3%
t 2035
 
5.4%
r 1876
 
5.0%
l 1804
 
4.8%
g 915
 
2.4%
Other values (34) 11649
31.2%
Common
ValueCountFrequency (%)
3607
97.8%
. 80
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3762
 
9.2%
3607
 
8.8%
s 3530
 
8.6%
e 3275
 
8.0%
i 3021
 
7.4%
o 2800
 
6.8%
n 2714
 
6.6%
t 2035
 
5.0%
r 1876
 
4.6%
l 1804
 
4.4%
Other values (36) 12644
30.8%

away_team_errors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5800813
Minimum0
Maximum5
Zeros1407
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:29.051226image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79322671
Coefficient of variation (CV)1.3674406
Kurtosis2.1811828
Mean0.5800813
Median Absolute Deviation (MAD)0
Skewness1.4582673
Sum1427
Variance0.62920861
MonotonicityNot monotonic
2023-02-03T13:34:29.170600image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1407
57.2%
1 765
31.1%
2 215
 
8.7%
3 61
 
2.5%
4 11
 
0.4%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1407
57.2%
1 765
31.1%
2 215
 
8.7%
3 61
 
2.5%
4 11
 
0.4%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 11
 
0.4%
3 61
 
2.5%
2 215
 
8.7%
1 765
31.1%
0 1407
57.2%

away_team_hits
Real number (ℝ)

Distinct22
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7670732
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:29.302124image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q311
95-th percentile15
Maximum22
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5126873
Coefficient of variation (CV)0.40066819
Kurtosis0.13926584
Mean8.7670732
Median Absolute Deviation (MAD)2
Skewness0.51233243
Sum21567
Variance12.338972
MonotonicityNot monotonic
2023-02-03T13:34:29.434264image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
9 293
11.9%
7 287
11.7%
8 275
11.2%
10 238
9.7%
6 225
9.1%
5 197
8.0%
11 194
7.9%
4 135
 
5.5%
12 132
 
5.4%
13 113
 
4.6%
Other values (12) 371
15.1%
ValueCountFrequency (%)
1 7
 
0.3%
2 26
 
1.1%
3 83
 
3.4%
4 135
5.5%
5 197
8.0%
6 225
9.1%
7 287
11.7%
8 275
11.2%
9 293
11.9%
10 238
9.7%
ValueCountFrequency (%)
22 4
 
0.2%
21 1
 
< 0.1%
20 2
 
0.1%
19 12
 
0.5%
18 15
 
0.6%
17 28
 
1.1%
16 40
 
1.6%
15 66
2.7%
14 87
3.5%
13 113
4.6%

away_team_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4150407
Minimum0
Maximum21
Zeros156
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:29.577323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1053905
Coefficient of variation (CV)0.70336622
Kurtosis1.0089307
Mean4.4150407
Median Absolute Deviation (MAD)2
Skewness0.93889878
Sum10861
Variance9.64345
MonotonicityNot monotonic
2023-02-03T13:34:29.709233image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 347
14.1%
2 340
13.8%
4 308
12.5%
5 272
11.1%
1 265
10.8%
6 217
8.8%
7 179
7.3%
0 156
6.3%
8 115
 
4.7%
9 85
 
3.5%
Other values (10) 176
7.2%
ValueCountFrequency (%)
0 156
6.3%
1 265
10.8%
2 340
13.8%
3 347
14.1%
4 308
12.5%
5 272
11.1%
6 217
8.8%
7 179
7.3%
8 115
 
4.7%
9 85
 
3.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 5
 
0.2%
15 10
 
0.4%
14 7
 
0.3%
13 21
 
0.9%
12 25
 
1.0%
11 36
1.5%
10 69
2.8%

date
Real number (ℝ)

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1642276
Minimum0
Maximum6
Zeros277
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:29.833200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9971159
Coefficient of variation (CV)0.63115429
Kurtosis-1.2941421
Mean3.1642276
Median Absolute Deviation (MAD)2
Skewness-0.07904305
Sum7784
Variance3.9884717
MonotonicityNot monotonic
2023-02-03T13:34:29.942834image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 396
16.1%
4 394
16.0%
6 392
15.9%
2 379
15.4%
1 374
15.2%
0 277
11.3%
3 248
10.1%
ValueCountFrequency (%)
0 277
11.3%
1 374
15.2%
2 379
15.4%
3 248
10.1%
4 394
16.0%
5 396
16.1%
6 392
15.9%
ValueCountFrequency (%)
6 392
15.9%
5 396
16.1%
4 394
16.0%
3 248
10.1%
2 379
15.4%
1 374
15.2%
0 277
11.3%

game_duration
Real number (ℝ)

Distinct168
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.03285
Minimum84
Maximum389.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:30.100920image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile166.4
Q1186.4
median200
Q3212.8
95-th percentile255.2
Maximum389.6
Range305.6
Interquartile range (IQR)26.4

Descriptive statistics

Standard deviation28.071184
Coefficient of variation (CV)0.13825932
Kurtosis5.5423233
Mean203.03285
Median Absolute Deviation (MAD)13.6
Skewness1.4872889
Sum499460.8
Variance787.99137
MonotonicityNot monotonic
2023-02-03T13:34:30.387058image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
208 55
 
2.2%
209.6 54
 
2.2%
180 53
 
2.2%
206.4 51
 
2.1%
192.8 47
 
1.9%
186.4 47
 
1.9%
184.8 46
 
1.9%
188 46
 
1.9%
203.2 45
 
1.8%
191.2 45
 
1.8%
Other values (158) 1971
80.1%
ValueCountFrequency (%)
84 1
 
< 0.1%
123.2 1
 
< 0.1%
129.6 1
 
< 0.1%
131.2 1
 
< 0.1%
132.8 2
0.1%
136 4
0.2%
137.6 3
0.1%
139.2 3
0.1%
140.8 2
0.1%
142.4 2
0.1%
ValueCountFrequency (%)
389.6 1
< 0.1%
380.8 1
< 0.1%
376.8 1
< 0.1%
375.2 1
< 0.1%
354.4 1
< 0.1%
341.6 1
< 0.1%
340 2
0.1%
336.8 1
< 0.1%
332.8 1
< 0.1%
331.2 1
< 0.1%

game_type
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Night Game, on grass
1560 
Day Game, on grass
733 
Night Game, on turf
 
104
Day Game, on turf
 
63

Length

Max length20
Median length20
Mean length19.284959
Min length17

Characters and Unicode

Total characters47441
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight Game, on grass
2nd rowNight Game, on grass
3rd rowNight Game, on grass
4th rowNight Game, on grass
5th rowDay Game, on grass

Common Values

ValueCountFrequency (%)
Night Game, on grass 1560
63.4%
Day Game, on grass 733
29.8%
Night Game, on turf 104
 
4.2%
Day Game, on turf 63
 
2.6%

Length

2023-02-03T13:34:30.570122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T13:34:30.724825image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
game 2460
25.0%
on 2460
25.0%
grass 2293
23.3%
night 1664
16.9%
day 796
 
8.1%
turf 167
 
1.7%

Most occurring characters

ValueCountFrequency (%)
7380
15.6%
a 5549
11.7%
s 4586
9.7%
g 3957
 
8.3%
e 2460
 
5.2%
n 2460
 
5.2%
G 2460
 
5.2%
m 2460
 
5.2%
r 2460
 
5.2%
, 2460
 
5.2%
Other values (9) 11209
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32681
68.9%
Space Separator 7380
 
15.6%
Uppercase Letter 4920
 
10.4%
Other Punctuation 2460
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5549
17.0%
s 4586
14.0%
g 3957
12.1%
e 2460
7.5%
n 2460
7.5%
m 2460
7.5%
r 2460
7.5%
o 2460
7.5%
t 1831
 
5.6%
i 1664
 
5.1%
Other values (4) 2794
8.5%
Uppercase Letter
ValueCountFrequency (%)
G 2460
50.0%
N 1664
33.8%
D 796
 
16.2%
Space Separator
ValueCountFrequency (%)
7380
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37601
79.3%
Common 9840
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5549
14.8%
s 4586
12.2%
g 3957
10.5%
e 2460
 
6.5%
n 2460
 
6.5%
G 2460
 
6.5%
m 2460
 
6.5%
r 2460
 
6.5%
o 2460
 
6.5%
t 1831
 
4.9%
Other values (7) 6918
18.4%
Common
ValueCountFrequency (%)
7380
75.0%
, 2460
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7380
15.6%
a 5549
11.7%
s 4586
9.7%
g 3957
 
8.3%
e 2460
 
5.2%
n 2460
 
5.2%
G 2460
 
5.2%
m 2460
 
5.2%
r 2460
 
5.2%
, 2460
 
5.2%
Other values (9) 11209
23.6%

home_team
Categorical

HIGH CORRELATION  UNIFORM 

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Cleveland Indians
 
89
Chicago Cubs
 
89
Los Angeles Dodgers
 
86
Toronto Blue Jays
 
86
Washington Nationals
 
84
Other values (25)
2026 

Length

Max length29
Median length19
Mean length16.695528
Min length12

Characters and Unicode

Total characters41071
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKansas City Royals
2nd rowCincinnati Reds
3rd rowBaltimore Orioles
4th rowAtlanta Braves
5th rowArizona Diamondbacks

Common Values

ValueCountFrequency (%)
Cleveland Indians 89
 
3.6%
Chicago Cubs 89
 
3.6%
Los Angeles Dodgers 86
 
3.5%
Toronto Blue Jays 86
 
3.5%
Washington Nationals 84
 
3.4%
Texas Rangers 83
 
3.4%
San Francisco Giants 83
 
3.4%
Boston Red Sox 82
 
3.3%
Kansas City Royals 81
 
3.3%
Cincinnati Reds 81
 
3.3%
Other values (20) 1616
65.7%

Length

2023-02-03T13:34:30.855444image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago 169
 
2.8%
los 167
 
2.8%
angeles 167
 
2.8%
san 164
 
2.7%
sox 162
 
2.7%
new 162
 
2.7%
york 162
 
2.7%
indians 89
 
1.5%
cleveland 89
 
1.5%
cubs 89
 
1.5%
Other values (57) 4646
76.6%

Most occurring characters

ValueCountFrequency (%)
a 3770
 
9.2%
3606
 
8.8%
s 3530
 
8.6%
e 3277
 
8.0%
i 3014
 
7.3%
o 2795
 
6.8%
n 2724
 
6.6%
t 2033
 
4.9%
r 1872
 
4.6%
l 1810
 
4.4%
Other values (36) 12640
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31399
76.5%
Uppercase Letter 5985
 
14.6%
Space Separator 3606
 
8.8%
Other Punctuation 81
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3770
12.0%
s 3530
11.2%
e 3277
10.4%
i 3014
9.6%
o 2795
8.9%
n 2724
8.7%
t 2033
 
6.5%
r 1872
 
6.0%
l 1810
 
5.8%
d 913
 
2.9%
Other values (14) 5661
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 671
11.2%
A 653
10.9%
B 492
 
8.2%
R 489
 
8.2%
S 488
 
8.2%
M 484
 
8.1%
T 411
 
6.9%
P 403
 
6.7%
D 328
 
5.5%
L 248
 
4.1%
Other values (10) 1318
22.0%
Space Separator
ValueCountFrequency (%)
3606
100.0%
Other Punctuation
ValueCountFrequency (%)
. 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37384
91.0%
Common 3687
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3770
 
10.1%
s 3530
 
9.4%
e 3277
 
8.8%
i 3014
 
8.1%
o 2795
 
7.5%
n 2724
 
7.3%
t 2033
 
5.4%
r 1872
 
5.0%
l 1810
 
4.8%
d 913
 
2.4%
Other values (34) 11646
31.2%
Common
ValueCountFrequency (%)
3606
97.8%
. 81
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3770
 
9.2%
3606
 
8.8%
s 3530
 
8.6%
e 3277
 
8.0%
i 3014
 
7.3%
o 2795
 
6.8%
n 2724
 
6.6%
t 2033
 
4.9%
r 1872
 
4.6%
l 1810
 
4.4%
Other values (36) 12640
30.8%

home_team_errors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58617886
Minimum0
Maximum5
Zeros1416
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:30.980544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80581712
Coefficient of variation (CV)1.3746949
Kurtosis2.0546943
Mean0.58617886
Median Absolute Deviation (MAD)0
Skewness1.4410193
Sum1442
Variance0.64934123
MonotonicityNot monotonic
2023-02-03T13:34:31.099430image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1416
57.6%
1 732
29.8%
2 241
 
9.8%
3 57
 
2.3%
4 13
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1416
57.6%
1 732
29.8%
2 241
 
9.8%
3 57
 
2.3%
4 13
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 13
 
0.5%
3 57
 
2.3%
2 241
 
9.8%
1 732
29.8%
0 1416
57.6%

home_team_hits
Real number (ℝ)

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6113821
Minimum0
Maximum22
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:31.231750image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median8
Q311
95-th percentile15
Maximum22
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4386792
Coefficient of variation (CV)0.39931792
Kurtosis0.18147362
Mean8.6113821
Median Absolute Deviation (MAD)2
Skewness0.47623304
Sum21184
Variance11.824515
MonotonicityNot monotonic
2023-02-03T13:34:31.356121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 294
12.0%
7 275
11.2%
9 274
11.1%
6 257
10.4%
10 236
9.6%
11 194
7.9%
5 184
7.5%
12 165
6.7%
4 151
6.1%
13 96
 
3.9%
Other values (13) 334
13.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12
 
0.5%
2 33
 
1.3%
3 72
 
2.9%
4 151
6.1%
5 184
7.5%
6 257
10.4%
7 275
11.2%
8 294
12.0%
9 274
11.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 3
 
0.1%
20 1
 
< 0.1%
19 10
 
0.4%
18 17
 
0.7%
17 28
 
1.1%
16 31
 
1.3%
15 36
 
1.5%
14 89
3.6%
13 96
3.9%

home_team_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5203252
Minimum0
Maximum17
Zeros130
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:31.482691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1125024
Coefficient of variation (CV)0.68855719
Kurtosis0.83058099
Mean4.5203252
Median Absolute Deviation (MAD)2
Skewness0.92009549
Sum11120
Variance9.6876713
MonotonicityNot monotonic
2023-02-03T13:34:31.611896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 354
14.4%
2 312
12.7%
4 305
12.4%
5 301
12.2%
1 273
11.1%
6 207
8.4%
7 190
7.7%
0 130
 
5.3%
8 130
 
5.3%
9 79
 
3.2%
Other values (8) 179
7.3%
ValueCountFrequency (%)
0 130
 
5.3%
1 273
11.1%
2 312
12.7%
3 354
14.4%
4 305
12.4%
5 301
12.2%
6 207
8.4%
7 190
7.7%
8 130
 
5.3%
9 79
 
3.2%
ValueCountFrequency (%)
17 4
 
0.2%
16 5
 
0.2%
15 4
 
0.2%
14 16
 
0.7%
13 26
 
1.1%
12 33
 
1.3%
11 37
 
1.5%
10 54
2.2%
9 79
3.2%
8 130
5.3%

start_time
Real number (ℝ)

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.044715
Minimum11
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-02-03T13:34:31.745064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q113
median19
Q319
95-th percentile19
Maximum21
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7123954
Coefficient of variation (CV)0.1591341
Kurtosis-1.1237023
Mean17.044715
Median Absolute Deviation (MAD)0
Skewness-0.82212624
Sum41930
Variance7.3570888
MonotonicityNot monotonic
2023-02-03T13:34:31.862248image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19 1347
54.8%
13 515
 
20.9%
18 235
 
9.6%
12 124
 
5.0%
16 78
 
3.2%
14 42
 
1.7%
20 41
 
1.7%
15 35
 
1.4%
17 35
 
1.4%
21 6
 
0.2%
ValueCountFrequency (%)
11 2
 
0.1%
12 124
 
5.0%
13 515
 
20.9%
14 42
 
1.7%
15 35
 
1.4%
16 78
 
3.2%
17 35
 
1.4%
18 235
 
9.6%
19 1347
54.8%
20 41
 
1.7%
ValueCountFrequency (%)
21 6
 
0.2%
20 41
 
1.7%
19 1347
54.8%
18 235
 
9.6%
17 35
 
1.4%
16 78
 
3.2%
15 35
 
1.4%
14 42
 
1.7%
13 515
 
20.9%
12 124
 
5.0%

venue
Categorical

Distinct31
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Progressive Field
 
89
Wrigley Field
 
89
Dodger Stadium
 
86
Rogers Centre
 
86
Nationals Park
 
84
Other values (26)
2026 

Length

Max length32
Median length20
Mean length16.528049
Min length9

Characters and Unicode

Total characters40659
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row Kauffman Stadium
2nd row Great American Ball Park
3rd row Oriole Park at Camden Yards
4th row Turner Field
5th row Chase Field

Common Values

ValueCountFrequency (%)
Progressive Field 89
 
3.6%
Wrigley Field 89
 
3.6%
Dodger Stadium 86
 
3.5%
Rogers Centre 86
 
3.5%
Nationals Park 84
 
3.4%
Globe Life Park in Arlington 83
 
3.4%
AT&T Park 83
 
3.4%
Fenway Park 82
 
3.3%
Kauffman Stadium 81
 
3.3%
Great American Ball Park 81
 
3.3%
Other values (21) 1616
65.7%

Length

2023-02-03T13:34:32.004231image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
park 1059
 
17.0%
field 824
 
13.2%
stadium 410
 
6.6%
iii 162
 
2.6%
progressive 89
 
1.4%
wrigley 89
 
1.4%
rogers 86
 
1.4%
centre 86
 
1.4%
dodger 86
 
1.4%
nationals 84
 
1.4%
Other values (42) 3247
52.2%

Most occurring characters

ValueCountFrequency (%)
6222
15.3%
a 3661
 
9.0%
e 3300
 
8.1%
i 2877
 
7.1%
r 2717
 
6.7%
l 2212
 
5.4%
d 1725
 
4.2%
n 1633
 
4.0%
o 1321
 
3.2%
t 1312
 
3.2%
Other values (36) 13679
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27325
67.2%
Uppercase Letter 6788
 
16.7%
Space Separator 6222
 
15.3%
Other Punctuation 243
 
0.6%
Dash Punctuation 81
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3661
13.4%
e 3300
12.1%
i 2877
10.5%
r 2717
9.9%
l 2212
8.1%
d 1725
 
6.3%
n 1633
 
6.0%
o 1321
 
4.8%
t 1312
 
4.8%
k 1302
 
4.8%
Other values (13) 5265
19.3%
Uppercase Letter
ValueCountFrequency (%)
P 1309
19.3%
F 907
13.4%
C 893
13.2%
S 571
8.4%
A 490
 
7.2%
I 486
 
7.2%
T 408
 
6.0%
M 323
 
4.8%
B 244
 
3.6%
N 164
 
2.4%
Other values (9) 993
14.6%
Other Punctuation
ValueCountFrequency (%)
. 160
65.8%
& 83
34.2%
Space Separator
ValueCountFrequency (%)
6222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34113
83.9%
Common 6546
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3661
 
10.7%
e 3300
 
9.7%
i 2877
 
8.4%
r 2717
 
8.0%
l 2212
 
6.5%
d 1725
 
5.1%
n 1633
 
4.8%
o 1321
 
3.9%
t 1312
 
3.8%
P 1309
 
3.8%
Other values (32) 12046
35.3%
Common
ValueCountFrequency (%)
6222
95.1%
. 160
 
2.4%
& 83
 
1.3%
- 81
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6222
15.3%
a 3661
 
9.0%
e 3300
 
8.1%
i 2877
 
7.1%
r 2717
 
6.7%
l 2212
 
5.4%
d 1725
 
4.2%
n 1633
 
4.0%
o 1321
 
3.2%
t 1312
 
3.2%
Other values (36) 13679
33.6%

Interactions

2023-02-03T13:34:26.138755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:11.312234image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.724745image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.202676image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.705008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:17.430169image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:19.131798image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:20.886090image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.517110image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:24.199081image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:26.286002image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:11.458295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.865945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.388065image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.847342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:17.599245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:19.283407image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:21.051310image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.666672image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:24.356892image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:26.435642image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:11.608129image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.023984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.524111image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.996022image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:17.761710image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:19.478055image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:21.226790image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.833412image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:24.544339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:26.576089image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:11.734372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.164690image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.659506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:16.366163image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:17.903121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:19.647312image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:21.371516image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.988569image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:24.724648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:26.718085image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:11.874614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.301222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.796737image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:16.497020image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:18.065509image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:19.812996image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:21.541995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:23.141846image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:24.885941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:27.155661image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.030673image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.462005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.940700image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:16.637008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:18.234066image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:20.003708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:21.711565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:23.313010image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:25.081486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:27.314514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.170140image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.614615image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.109750image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:16.788028image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:18.396533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:20.164902image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:21.866533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:23.475386image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:25.255502image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:27.461976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.308144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.750328image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.255751image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:16.939504image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:18.570541image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:20.360606image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.015272image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:23.637225image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:25.471587image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:27.600379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.449281image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:13.914428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.387893image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:17.110477image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:18.738663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:20.535831image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.160879image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:23.848691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:25.650176image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:27.761925image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:12.599072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:14.069787image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:15.530367image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:17.289844image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:18.936129image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:20.717983image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:22.352970image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:24.029726image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-02-03T13:34:25.910807image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-02-03T13:34:32.144071image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
attendanceaway_team_errorsaway_team_hitsaway_team_runsdategame_durationhome_team_errorshome_team_hitshome_team_runsstart_timeaway_teamgame_typehome_teamvenue
attendance1.0000.018-0.042-0.0490.2500.051-0.0180.0010.024-0.1010.1190.2710.4250.426
away_team_errors0.0181.0000.0330.0470.0410.1050.0050.1450.2060.0140.0330.0000.0000.000
away_team_hits-0.0420.0331.0000.759-0.0270.4060.1660.1010.046-0.0160.0000.0000.0670.067
away_team_runs-0.0490.0470.7591.000-0.0200.3720.2570.0870.035-0.0120.0300.0000.0640.062
date0.2500.041-0.027-0.0201.0000.010-0.0040.0200.003-0.4230.0000.3560.0000.000
game_duration0.0510.1050.4060.3720.0101.0000.1430.2990.207-0.0200.0230.0300.0640.061
home_team_errors-0.0180.0050.1660.257-0.0040.1431.000-0.019-0.010-0.0250.0360.0000.0490.046
home_team_hits0.0010.1450.1010.0870.0200.299-0.0191.0000.747-0.0340.0400.0000.0600.059
home_team_runs0.0240.2060.0460.0350.0030.207-0.0100.7471.000-0.0030.0080.0270.0380.034
start_time-0.1010.014-0.016-0.012-0.423-0.020-0.025-0.034-0.0031.0000.0440.5800.2430.248
away_team0.1190.0330.0000.0300.0000.0230.0360.0400.0080.0441.0000.1360.1790.179
game_type0.2710.0000.0000.0000.3560.0300.0000.0000.0270.5800.1361.0000.5740.574
home_team0.4250.0000.0670.0640.0000.0640.0490.0600.0380.2430.1790.5741.0001.000
venue0.4260.0000.0670.0620.0000.0610.0460.0590.0340.2480.1790.5741.0001.000

Missing values

2023-02-03T13:34:27.990918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-03T13:34:28.315589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsdategame_durationgame_typehome_teamhome_team_errorshome_team_hitshome_team_runsstart_timevenue
040030New York Mets1736200.8Night Game, on grassKansas City Royals09419Kauffman Stadium
121621Philadelphia Phillies0522156.8Night Game, on grassCincinnati Reds08319Great American Ball Park
212622Minnesota Twins0522197.6Night Game, on grassBaltimore Orioles09419Oriole Park at Camden Yards
318531Washington Nationals0832204.8Night Game, on grassAtlanta Braves18119Turner Field
418572Colorado Rockies1842182.4Day Game, on grassArizona Diamondbacks08312Chase Field
528386Seattle Mariners111101228.0Night Game, on grassTexas Rangers17219Globe Life Park in Arlington
612757Toronto Blue Jays0921191.2Night Game, on turfTampa Bay Rays17319Tropicana Field
728329Los Angeles Dodgers0631177.6Night Game, on grassSan Diego Padres12019Petco Park
826049St. Louis Cardinals1851223.2Night Game, on grassPittsburgh Pirates212619PNC Park
910478Chicago White Sox01151224.8Night Game, on grassOakland Athletics010419Oakland-Alameda County Coliseum
attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsdategame_durationgame_typehome_teamhome_team_errorshome_team_hitshome_team_runsstart_timevenue
245043683Philadelphia Phillies2620209.6Day Game, on grassCincinnati Reds06616Great American Ball Park
245145785Minnesota Twins0720196.8Day Game, on grassBaltimore Orioles010316Oriole Park at Camden Yards
245248282Washington Nationals0840216.8Day Game, on grassAtlanta Braves24316Turner Field
245348165Colorado Rockies015100257.6Night Game, on grassArizona Diamondbacks012518Chase Field
245444020Chicago Cubs01190192.8Night Game, on grassLos Angeles Angels of Anaheim13019Angel Stadium of Anaheim
245531042Toronto Blue Jays2756201.6Day Game, on turfTampa Bay Rays17316Tropicana Field
245639500St. Louis Cardinals0516183.2Day Game, on grassPittsburgh Pirates19413PNC Park
245720098San Francisco Giants0632210.4Day Game, on grassMilwaukee Brewers29412Miller Park
245817883Detroit Tigers01372215.2Day Game, on grassMiami Marlins110316Marlins Park
245910298Boston Red Sox11062226.4Night Game, on grassCleveland Indians09718Progressive Field